Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning

XUE Qing; LAI Dong; XU Yongjun; YAN Li

doi:10.11999/JEIT221536

Volume 46 Issue 1

Jan. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 138-145

XUE Qing, LAI Dong, XU Yongjun, YAN Li. Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(1): 138-145. doi: 10.11999/JEIT221536

Citation:

XUE Qing, LAI Dong, XU Yongjun, YAN Li. Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(1): 138-145. doi: 10.11999/JEIT221536

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Beam Configuration for Millimeter Wave Communication Systems Based on Distributed Federated Learning

doi: 10.11999/JEIT221536

XUE Qing^{1, 2
,
,},
LAI Dong¹,
XU Yongjun¹,
YAN Li³

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau 999078, China
3.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

Funds: The National Natural Science Foundation of China (62001071, 62101460, 62271094), Macao Young Scholars Program (AM2021018), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), China Postdoctoral Science Foundation (2022MD723725)

Received Date: 2022-12-13
Rev Recd Date: 2023-05-08

Available Online: 2023-05-17

Publish Date: 2024-01-17

Abstract

Abstract

Considering the complex beam configuration problem of ultra-dense millimeter wave communication systems, a Beam management Method based on Distributed Federation Learning (BMDFL) is proposed to maximize the beam coverage by using the limited beam resources. To solve the problem of user data security in traditional centralized learning, the system model is constructed based on DFL, which can reduce the leakage of user privacy information. In order to realize intelligent configuration of beams, Double Deep Q-Network (DDQN) is introduced to train the system model, and the long-term dynamic optimization problem is transformed into the corresponding mathematical model through the Markov decision process. Simulation results demonstrate the effectiveness and robustness of the proposed method in terms of network throughput and user coverage.
- Millimeter wave communication,
- Beam configuration,
- Ultra-dense network,
- Distributed Federation Learning (DFL),
- Reinforcement learning

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References(13)

References

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